Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning

Abstract

The Combined Algorithm Selection and Hyperparameter optimization (CASH) is a challenging resource allocation problem in the field of AutoML. We propose MaxUCB, a max $k$-armed bandit method to trade off exploring different model classes and conducting hyperparameter optimization. MaxUCB is specifically designed for the light-tailed and bounded reward distributions arising in this setting and, thus, provides an efficient alternative compared to classic max $k$-armed bandit methods assuming heavy-tailed reward distributions. We theoretically and empirically evaluate our method on four standard AutoML benchmarks, demonstrating superior performance over prior approaches. We make our code and data available at https://github.com/amirbalef/CASH_with_Bandits

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Cite

Text

Balef et al. "Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning." Advances in Neural Information Processing Systems, 2025.

Markdown

[Balef et al. "Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/balef2025neurips-put/)

BibTeX

@inproceedings{balef2025neurips-put,
  title     = {{Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning}},
  author    = {Balef, Amir Rezaei and Vernade, Claire and Eggensperger, Katharina},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2025},
  url       = {https://mlanthology.org/neurips/2025/balef2025neurips-put/}
}